Predicting psychometric profiles from behavioral data using machine-learning while maintaining user anonymity

A machine-implemented method comprising:

• accepting from an engagement-measuring instrument engagement data on users who engage with a particular stimulus and for whom predicted psychometric models are stored;
• retrieving stored psychometric models of users whose engagement data are accepted;
• and
• training at least one machine-learning method to determine an engagement model that predicts a measure of a likelihood of engagement for a user whose engagement data may be unknown, based on the psychometric model of the user whose engagement data may be unknown, the training using both accepted engagement data on the users whose psychometric models are retrieved and the retrieved psychometric models,
• wherein each psychometric model of a specific user is a predicted psychometric profile of the user, and comprises a set of dimensions including at least one purely psychometric dimension and optionally at least one demographic dimension of the user, obtained while maintaining ignorance of personally identifiable information on the specific user.

A system comprising:

• (a) a measuring instrument configured to measure psychometric dimensions of users;
• (b) a psychometric data analytics engine (PDAE) coupled to the measuring instrument, the PDAE comprising:
• (i) a processor set comprising at least one processor;
• and
• (ii) a storage subsystem,
• wherein the storage subsystem comprises a non-transitory machine-readable medium having stored therein code (187, 188, 189) that when executed by at least one processor of the processor set, carries out a method comprising:
• (a) accepting automatically-machine-collected data about online behavior of users of a first set of users;
• (b) accepting measured psychometric dimensions of users of the set of users to form accepted and measured psychometric profiles of users of the first set, each psychometric profile comprising a set of dimensions including at least one purely psychometric dimension and optionally at least one demographic dimension, the measured psychometric dimension obtained from a measuring instrument;
• (c) using the accepted data about online behavior and the corresponding accepted measured psychometric profiles of the users of the first set to train at least one machine-learning method of predicting psychometric profiles of users whose psychometric profiles may be unknown, the at least one method of predicting for any user whose psychometric profile may be unknown using automatically-machine-collected data about online behavior of the user whose psychometric profile may be unknown;
• (d) accepting automatically-machine-collected data about online behavior of users of a population of users whose psychometric profiles may be unknown, the accepted automatically-machine-collected data excluding any personally identifiable information;
• (e) using at least one of the trained at least one machine-learning method of predicting to generate psychometric models of each of the population of users from the accepted data about online behavior of the users of the population;
• and
• (f) storing the predicted psychometric models,
• wherein no personally identifiable information of users of the population needs to be used or maintained, such that the method is able to maintain anonymity of each of the users of the population of users.

A system comprising:

• (a) a measuring instrument configured to measure psychometric dimensions of users;
• (b) a psychometric data analytics engine (PDAE) coupled to the measuring instrument, the PDAE comprising:
• (i) a controller;
• (ii) a storage subsystem coupled to the controller;
• (iii) an interface coupled to the controller and the storage subsystem, and configured to interface the PDAE with at least the measuring instrument and a network,
• the interface under control of the controller being configured to accept from the measuring instrument measured psychometric dimensions of users of a first set of users to form accepted psychometric profiles of users of the first set, each psychometric profile comprising a set of dimensions including at least one purely psychometric dimension and optionally at least one demographic dimension,
• the interface under control of the controller further being configured to accept via the network automatically-machine-collected data about online behavior of users of a second set of users to form summary data about online behavior, each user of the second set also being in the first set;
• (iv) a machine-learning engine coupled to the controller and configured to carry out at least one machine-learning method;
• and
• (v) a psychometric engine coupled to the controller and the machine-learning engine, and configured under control of the controller to use the summary data about online behavior and the corresponding accepted measured psychometric profiles of the users of the second set to cause training, using the machine-learning engine, of at least one respective machine-learning method of predicting each respective dimension of psychometric profiles of users whose psychometric profiles may be unknown,
• wherein the interface, under control of the controller also is configured to accept automatically-machine-collected data about online behavior of users of a third set of users whose psychometric profiles may be unknown, to form summary data about online behavior of the users of the third set,
• wherein the PDAE, under control of the controller is configured to use at least one of the trained machine-learning methods of predicting to generate psychometric models of each of the third set of users from the summary data about online behavior of the users of the third set, and to store the predicted psychometric models, and
• wherein the PDAE is configured to maintain ignorance of personally identifiable information of each of the users of the first, second, and third sets of users.